Introductory Statistics for Business and Economics, 4th Edition,9780471615170

Introductory Statistics for Business and Economics, 4th Edition

by ;
Edition: 4th
ISBN13: 9780471615170
ISBN10: 047161517X
Format: Hardcover
Pub. Date: 1990-01-01
Publisher(s): Wiley
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Summary

This Fourth Edition includes new sections on graphs, robust estimation, expected value and the bootstrap, in addition to new material on the use of computers. The regression model is well covered, including both nonlinear and multiple regression. The chapters contain many real-life examples and are relatively self-contained, making adaptable to a variety of courses.

Table of Contents

PART I BASIC PROBABILITY AND STATISTICS 1(186)
The Nature of Statistics
3(22)
Random Sampling: A Political Poll
4(6)
Randomized Experiments: Testing a Hospital Routine
10(4)
Observational Studies vs. Randomized Experiments
14(6)
Brief Outline of the Book
20(5)
Summary
20(5)
Descriptive Statistics
25(44)
Frequency Tables and Graphs
26(6)
Center of a Distribution
32(7)
Spread of a Distribution
39(7)
Statistics by Computer
46(1)
Linear Transformations
47(5)
Calculations Using Relative Frequencies
52(1)
The Use and Misuse of Graphs
53(16)
Summary
64(5)
Probability
69(40)
Introduction
70(3)
Probability Models
73(6)
Compound Events
79(6)
Conditional Probability
85(4)
Independence
89(4)
Bayes Theorem: Tree Reversal
93(6)
Other Views of Probability
99(10)
Summary
103(6)
Probability Distributions
109(44)
Discrete Random Variables
110(3)
Mean and Variance
113(3)
The Binomial Distribution
116(8)
Continuous Distributions
124(3)
The Normal Distribution
127(7)
A Function of a Random Variable
134(7)
Expected Value in Bidding
141(12)
Summary
146(7)
Two Random Variables
153(34)
Distributions
154(7)
A Function of Two Random Variables
161(3)
Covariance
164(6)
Linear Combination of Two Random Variables
170(17)
Summary
176(6)
Review Problems (Chapters 1-5)
182(5)
PART II INFERENCE FOR MEANS AND PROPORTIONS 187(168)
Sampling
189(42)
Random Sampling
190(6)
Moments of the Sample Mean
196(3)
The Shape of the Sampling Distribution
199(8)
Proportions (Percentages)
207(8)
Small-Population Sampling
215(3)
Monte Carlo
218(13)
Summary
225(6)
Point Estimation
231(22)
Populations and Samples
232(1)
Efficiency of Unbiased Estimators
232(7)
Efficiency of Biased and Unbiased Estimators
239(5)
Consistent Estimators
244(9)
Summary
248(5)
Confidence Intervals
253(34)
A Single Mean
254(7)
Small-Sample t
261(4)
Difference in Two Means, Independent Samples
265(3)
Difference in Two Means, Matched Samples
268(5)
Proportions
273(4)
The Bootstrap
277(10)
Summary
281(6)
Hypothesis Testing
287(37)
Hypothesis Testing Using Confidence Intervals
288(5)
p-Value (One-Sided)
293(7)
Classical Hypothesis Tests
300(6)
Classical Tests Reconsidered
306(4)
Operating Characteristics Curve (OCC)
310(4)
Two-Sided Tests
314(10)
Summary
320(4)
Analysis of Variance (ANOVA)
324(31)
One-Way ANOVA
325(11)
Two-Way ANOVA
336(7)
Confidence Intervals
343(12)
Summary
346(4)
Review Problems (Chapters 6-10)
350(5)
PART III REGRESSION: RELATING TWO OR MORE VARIABLES 355(160)
Fitting a Line
357(14)
Introduction
358(2)
Ordinary Least Squares (OLS)
360(6)
Advantages of OLS and WLS
366(5)
Summary
368(3)
Simple Regression
371(25)
The Regression Model
372(3)
Sampling Variability
375(4)
Confidence Intervals and Tests for β
379(4)
Predicting Y at a Given level of X
383(6)
Extending the Model
389(7)
Summary
390(6)
Multiple Regression
396(38)
Why Multiple Regression?
397(3)
The Regression Model and Its OLS Fit
400(6)
Confidence Intervals and Statistical Tests
406(4)
Regression Coefficients as Multiplication Factors
410(7)
Simple and Multiple Regression Compared
417(7)
Path Analysis
424(10)
Summary
428(6)
Regression Extensions
434(40)
Dummy (0-1) Variables
435(10)
Analysis of Variance (ANOVA) by Regression
445(4)
Simplest Nonlinear Regression
449(3)
Nonlinearity Removed by Logs
452(9)
Diagnosis by Residual Plots
461(13)
Summary
466(8)
Correlation
474(41)
Simple Correlation
475(7)
Correlation and Regression
482(7)
The Two Regression Lines
489(7)
Correlation in Multiple Regression
496(5)
Multicollinearity
501(14)
Summary
506(6)
Review Problems (Chapters 11-15)
512(3)
PART IV TOPICS IN CLASSICAL AND BAYESIAN INFERENCE 515(122)
Nonparametric and Robust Statistics (Requires Chapter 9)
517(32)
Introduction: Mean or Median?
518(1)
Sign Test for the Median
518(4)
Confidence Interval for the Median
522(3)
Wilcoxon Rank Test
525(3)
Rank Tests in General
528(5)
Runs Test for Independence
533(3)
Robust Statistics: Trimming and Weighting
536(13)
Summary
545(4)
Chi-Square Tests (Requires Chapter 9)
549(15)
X2 Tests for Multinomials: Goodness of Fit
550(5)
X2 Tests for Independence: Contingency Tables
555(9)
Summary
561(3)
Maximum Likelihood Estimation (Requires Chapter 7)
564(18)
Introduction
565(1)
MLE for Some Familiar Cases
566(7)
MLE for the Uniform Distribution
573(3)
MLE in General
576(6)
Summary
579(3)
Bayesian Inference (Requires Chapter 8)
582(38)
Posterior Distributions
583(5)
The Population Proportion
588(10)
The Mean μ in a Normal Model
598(7)
The Slope β in Normal Regression
605(3)
Bayesian Shrinkage Estimates
608(7)
Classical and Bayesian Estimates Compared
615(5)
Summary
615(5)
Bayesian Decision Theory (Requires Chapter 19)
620(17)
Maximizing Gain (or Minimizing Loss)
621(6)
Point Estimation as a Decision
627(6)
Classical and Bayesian Statistics Compared
633(4)
Summary
635(2)
PART V SPECIAL TOPICS FOR BUSINESS AND ECONOMICS 637(100)
Decision Trees (Requires Chapter 3)
639(25)
The Basic Tree
640(8)
Testing to Revise Probabilities: Bayes Theorem
648(5)
Utility Theory to Handle Risk Aversion
653(11)
Summary
657(7)
Index Numbers (Requires Nothing Previous)
664(14)
Price Indexes
665(3)
Further Indexes
668(4)
Indexes in Practice
672(6)
Summary
675(3)
Sampling Designs (Requires Chapter 8)
678(14)
Stratified Sampling
679(8)
Other Sampling Designs
687(5)
Summary
690(2)
Time Series (Requires Chapter 15)
692(30)
Two Special Characteristics of a Time Series
693(2)
Decomposition and Forecasting Using Regression
695(10)
Traditional Ratio to Moving Average
705(5)
Forecasting Using Exponential Smoothing
710(2)
Forecasting Using Box-Jenkins Models
712(2)
Generalized Least Squares (GLS)
714(8)
Summary
719(3)
Simultaneous Equations (Requires Chapter 13)
722(15)
Introduction: The Bias in OLS
723(4)
The Remedy: Instrumental Variables (IV)
727(4)
Two-Stage Least Squares (2SLS)
731(6)
Summary
735(2)
Appendixes 737(29)
2-2 Careful Approximation of the Median
738(1)
2-5 Effects of a Linear Transformation: Proofs
738(1)
3-7 Probability as Axiomatic Mathematics
738(1)
4-2 Easier Formula for σ2: Proof
739(1)
4-3 Binomial Formula: Proof
739(2)
4-4 Calculus for Continuous Distributions
741(1)
5-3 Independent Implies Uncorrelated: Proof
742(1)
5-4 Linear Combinations: Proofs
742(1)
6-3 Central Limit Theorem
743(1)
6-4 Continuity Correction: Graphical Explanation
743(1)
7-2 Standard Error of X
743(1)
7-4 Consistency: Careful Definition
744(1)
8-3 Standard Error of (X1-X2): Proof
745(1)
8-5 Confidence Interval for π: Derivation of Graph
745(1)
9-2 A More Exact p-Value for Proportions
746(1)
10-1 Breakdown of Total SS: Proof
747(1)
10-2 Two-Way ANOVA, Breakdown of Total SS: Proof
747(1)
10-3 ANOVA Is Much More Than Just Testing H0
748(1)
11-1 Lines and Planes
748(2)
11-2 Least-Squares Formulas: Proofs
750(1)
12-2 The Moments of b: Proofs and Discussion
751(2)
12-3 A One-sided or Two-sided Test?
753(1)
12-4 Confidence Intervals above X0: Proofs
754(1)
13-2 Solution of a Set of Simultaneous Equations
755(1)
13-5 Direct Plus Indirect Relation: Proof
756(1)
14-4 Log Regression Handles a Multiplicative Error Term
756(1)
15-1 Correlation in Chapter 15 Agrees with Chapter 5
757(1)
15-2 ANOVA and r2: Proofs
757(1)
18-2 MLE for Some Familiar Cases: Proofs
758(2)
19-2 Bayesian Confidence Interval for π: Proof
760(1)
19-3 Posterior Distribution of μ in a Normal Model: Proof
761(1)
19-4 Posterior Distribution of β in Normal Regression: Proof
761(1)
19-5 Bayesian Shrinkage Confidence Intervals
762(1)
24-2 Serial Correlation and the Durbin-Watson Test
762(1)
24-3 Moving Averages in General
763(1)
24-4 Exponential Smoothing: Proof
764(1)
24-5 Forecasting Using Box-Jenkins Models
764(2)
Tables 766(15)
References 781(6)
Answers to Odd-Numbered Problems 787(16)
Glossary of Common Symbols 803(4)
Index of Examples and Problems 807(2)
Index 809

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